from sklearn.ensemble import RandomForestRegressor regressor = RandomForestRegressor(n_estimators=20, random_state=0) regressor.fit(X_train, y_train) y_pred = regressor.predict(X_test)
from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
from sklearn import metrics print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred)) print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred)) print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))
